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Friday, August 26, 2016

It often happens in science that our theory of some area of reality is very precise, but the reality is too complex to work out precisely, or analytically. This can be when we decide to use computer simulation of that reality to get at least a close approximation to the truth. When a phenomenon is determined by a precise process, then if we increase the complexity of our simulation, and if the simulation really is simulating the underlying reality, then the more computer power we apply, the closer we get to the truth--that is, our results approach that truth asymptotically.

For example, if you want to predict the rotation of galaxies in space relative to each other, and of the stars within the galaxies, the theories of physics will do the job, in principle. But solving the equations directly the way one does in algebra or calculus is not possible with so many variables. However, you can use a computer to simulate the movement and get a very good approximation (we've discussed this here, among other places). Thus, at each time interval, you take the position and motion of each object you want to follow, and those measures of nearby objects, and use Newton's law of gravity to predict the position of the objects one time interval later.

If the motion you simulate doesn't match what you can observe, you suspect you've got something wrong with the theory you are using. In the case of cosmology, one such factor is known as 'dark matter'. That can be built into models of galactic motion, to get better predictions. In this way, simulation can tell you something you didn't already know, and because the equations can't be directly solved, simulation is an approach of choice.

In many situations, even if you think that the underlying causal process is deterministic, measurements are imperfect, and you may need to add a random 'noise' factor to each iteration of your simulation. Each simulation will be slightly 'off' because of this, but you run the same simulation thousands of times, so the effect of the noise evens out, and the average result represents what you are trying to model.

Is life a simulation of life?
Just like other processes that we attempt to simulate, life is a complex reality. We try to explain it with the very general theory of evolution, and we use genetics to try to explain how complex traits evolve, but there are far too many variables to predict future directions and the like analytically. This is more than just because of biological complexity however, in part because the fundamental processes of life seem, as far as we can tell, inherently probabilistic (not just a matter of measurement error). This adds an additional twist that makes life itself seem to be a simulation of its underlying processes.

Life evolves by parents transmitting genes to offspring. For those genes to be transmitted to the next generation, the offspring have to live long enough, must be able to acquire mates, and must be able to reproduce. Genes vary because mutations arise. For simplicity's sake, let's say that successful mating requires not falling victim to natural selection before offspring are produced, and that that depends on an organism's traits, and that genes are causally responsible for those traits. In reality, there are other process to be considered, but these will illustrate our point.

Mutation and surviving natural selection seem to be probabilistic processes. If we want to simulate life, we have to specify the probability of a mutation along some simulated genome, and the probability that a bearer of the mutation survives and reproduces. Populations contain thousands of individuals, genomes incur thousands of mutations each generation, and reproductive success involves those same individuals. This is far too hard to write tractable equations for in most interesting situations, unless we make almost uselessly simplifying assumptions. So we simulate these phenomena.

How, basically, do we do this? Here, generically and simplified, but illustrating the issues, is the typical way (and the way taken by my own elaborate simulation program, called ForSimwhich is freely available):

For each individual in a simulated population, each generation, we draw a random number based on an assumed mutation rate, and add the resulting number and location of mutations to the genotype of the individual. Then for each resulting simulated genotype, we draw a random number from the probability that such a genotype reproduces, and either remove or keep the individual depending on the result. We keep doing this for thousands of generations, and see what happens. As an example, the box lists some of the parameter values one specifies for a program like ForSim.

Sometimes, if the simulation is accurate enough, the probability and other values we assume look like what ecologists or geneticists believe is going on in their field site or laboratory. In the case of humans, however, we have little such data, so we make a guess at what we think might have been the case during our evolution. Often these things are empirically estimated one at a time, but their real values affect each other in many ways. This is, of course, very far from the situation in physics, described above! Still, we at least have a computer-based way to approximate our idea of evolutionary and genetic processes.

We run this for many, usually many thousand generations, and see the trait and genomic causal pattern that results (we've blogged about some of these issues here, among other posts). This is a simulation since it seems to follow the principles we think are responsible for evolution and genetic function. However, there is a major difference.

Unlike simulations in astronomy, life really does seem to involve random draws for probabilistic processes. In that sense, life looks like it is, itself, a simulation of these processes. The random draws it makes are not just practical estimates of some underlying phenomenon, but manifestation of the actual probabilistic nature of the phenomenon.

This is important, because when we simulate a process, we know that its probabilistic component can lead to different results each time through. And yet, life itself is a one-time run of those processes. In that sense, life is a simulation but we can only guess at the underlying causal values (like mutation and survival rates) from the single set of data: what actually happened its one time through. Of course, we can test various examples, like looking at mutation rates in bacteria or in some samples of people, but these involve many problems and are at best general estimates from samples, often artificial or simplified samples.

But wait! Is life a simulation after all? If not, what is life?
I don't want us to be bogged down in pure semantics here, but I think the answer is that in a very profound way, life is not a simulation in the sense we're discussing. For the relevant variables, life is not based on an underlying theoretical process in the usual sense, of whose parameters we use random numbers to approximate in simulations.

For example, we evaluate biological data in terms of 'the' mutation rate in genomes from parent to offspring. But in fact, we know there is no such thing as 'the' mutation rate, one that applies to each nucleotide as it is replicated from one generation to the next, and from which each actual mutation is a random draw. The observed rate of mutation at a given location in a given sample of a given species' genomes depends among other things on the sex, the particular nucleotides surrounding the site in question (and hence all sites along the DNA string), and the nature of the mutation-detection proteins coded by that individual's genome, and mutagen levels in the environment. In our theory, and in our simulations, we assume an average rate, and that the variation from that average will, so to speak, 'average out' in our simulations.

But I think that is fundamentally wrong. In life, every condition today is a branch-point for the future. The functional implications of a mutation here and now, depend on the local circumstances, and that is built into the production of the future local generations. Life in fact does not 'average' over the genome and over individuals does not in fact generate what life does, but in a sense the opposite. Each event has its own local dynamics and contingencies, but the effect of those conditions affects the rates of events in the future. Everywhere it's different, and we have no theory about how different, especially over evolutionary time.

Indeed, one might say that the most fundamental single characteristic of life is that the variation generated here today is screened here today and not anyplace else or any time else. In that sense, each mutation is not drawn from the same distribution. The underlying causal properties vary everywhere and all the time. Sometimes the difference may be slight, but we can't count on that being true and, importantly, we have no way of knowing when and to what extent it's true.

The same applies to foxes and rabbits. Every time a fox chases a rabbit, the conditions (including the genotypes of the fox and rabbit) differ. The chance aspect of whether it's caught or not are not the same each time, the success 'rate' is not drawn from a single, fixed distribution. In reality, each chase is unique.

After the fact, we can look back at net results, and it's all too tempting to think of what we see as a steady, deterministic process with a bit of random noise thrown in. But that's not an accurate way to think, because we don't know how inaccurate it is, when each event is to some (un-prespecified) extent unique. Overall, life is not, in fact, drawing from an underlying distribution. It is ad hoc by its very nature and that's what makeslife different from other physical phenomena.

Life, and we who partake of it, are unique. The fact of local, contingent uniqueness is an important reason that the study of lifeeludes much of what makes modern physical science work. The latter's methods and concepts assume replicable law-like underlying regularity. That's the kind of thing we attempt to model, or simulate, by treating phenomena like mutation as if they are draws from some basic underlying causal distribution. But life's underlying regularity is its irregularity.

This means that one of the best ways we have of dealing with complex phenomena of life, simulating them by computer, smoothes over the very underlying process that we want to understand. In that sense, strangely, life appears to be a simulation but is even more elusive than that. To a great extent, except by some very broad generalities that are often too broad to be very useful, life isn't the way we simulate it, and doesn't even simulate itself in that way.

What would be a better approach to understanding life? The next generation will have to discover that.

Thursday, August 25, 2016

This is an abridged syllabus for my course this fall. Apologies for any formatting issues, but copying and pasting from Word into Blogger isn't a party. For background on my textbook-free approach and overall philosophy for teaching evolution, please see this post and the links therein. Cheers to all you learners, teachers, and professors!

Fall 2016

APG 201: Human Origins and Evolution

3 credits

Dr. Holly Dunsworth

Course Description

The biocultural evolution of humans. An investigation into humankind’s place in nature, including a review of the living primates, human genetics and development, evolutionary theory, and the human fossil record. Fulfills both the General Education outcomes A1 (STEM knowledge) and B4 (information literacy).

Required reading

•Your Inner Fish by Neil Shubin

•The Incredible Unlikeliness of Being by Alice Roberts

•Additional articles are linked in the syllabus, or posted on our course site on Sakai

Non-required reference

•Biological Anthropology, 3rd Edition by Stanford, et al. (2013, Pearson) – standard textbook (a copy is on reserve at the library, along with Shubin and Roberts)

Quizzes 1, 2, and 3 (15% each); Research Project (15%; a two part exercise in information literacy, evolutionary thinking, and writing); Portfolio (40%; a thin folder or binder containing all the assignments in chronological order.)

Schedule

Unit 1. Observe and Explain - This view of life. Our place in nature. What is the anthropological perspective? What about the biocultural? What is the scientific approach to understanding human origins? What is a human? What are human traits? How do humans fit on the Tree of Life? What is evolution?

7-Sep 1.1-Introduction to course (reflecting on knowledge to spark semester)

9-Sep1.2-Overview of course (syllabus, anthropology, etc...)

12-Sep1.3-Scientific process

14-Sep1.4-Linnaeus and the Order Primates

16-Sep1.5-Overview of Primate taxonomy; Diet

19-Sep1.6-Primate locomotion and encephalization

21-Sep1.7-Primate tool use and communication

23-Sep1.8-Primate sociality

26-Sep1.9-Evolution and Darwin's evidence

28-Sep1.10-Phylogeny

30-Sep1.11-no class today

3-Oct1.12-Modern evidence Darwin wishes he had

5-OctQuiz 1

Unit 2. Explain and Predict - Explaining the similarities and differences. How evolution works. Why are we like our parents but not exactly? Why are we like other species but not exactly? How did human traits and human variation evolve? How does evolution occur? How do we know what the last common ancestor (LCA) was like?

7-Oct2.1-Inheritance and gene expression, 1

10-Octn/a-Columbus Day, classes do not meet

12-Oct2.2-Inheritance and gene expression, 2

14-Oct2.3-Inheritance and gene expression, 3

17-Oct2.4-Mutation and gene flow

19-Oct2.5-Natural selection

21-Oct2.6-More natural selection; Genetic drift

24-Oct2.7-Malaria resistance and lactase persistence

26-Oct2.8-Building evolutionary scenarios

28-Oct2.9- Origins of Bipedalism; Species and speciation

31-Oct2.10-Genomics, molecular clocks, and the LCA

2-NovQuiz 2-

Unit 3. Test and Observe - Evolving humans, past and present. Ancient evidence for our extinct hominin relatives. Modern human origins and variation. The cultural controversy over evolution.How did human traits evolve? How and why do humans vary? Should we look to our ancestors as a lifestyle guide? Are we still evolving? Is evolution racist? Why is human evolution misunderstood and why is it controversial?

·In a half-page or
more: Write about your primate video viewing experience, for example, you might
write about what you saw, at face value, or you might want to write about what
defied your expectations or what surprised you, or what you would like to learn
more about. Also: Without looking at
any resources except for these films, come up with some categories for the
different types of primate locomotion, give those categories names and
definitions, and list which species in the films fall into which categories
you’ve created.

·In a half-page or
more: What the heck is this Sonic hedgehog thing that Shubin’s talking about?

2.2

Assigned Reading/viewing

·YIF, Chapter 4: Teeth Everywhere – Shubin

Portfolio Assignment

·In a half-page or
more: Teeth make better fossils than bones and so they preserve more often and
fill up the fossil record. If you want to do paleontology, you need to get excited
about teeth. Why are teeth exciting?

·In a half-page or
more: After reading the Shubin chapters… Is it fair to say that when you smell
something, that something is touching your brain? Why is it called the eyeless
gene if you can have it and still have eyes? How does hearing work? What does
your ear do besides hear, and how? What does drinking lots of alcohol do to
your ears?

·In a half-page or more:
Describe all the factors you can think of that contributed to the skin color
you have today, right now. Would you be answering this question, in this
course, if your skin color were different? Why or why not?

(Plus whatever we accomplished in class in the Skin Color workbook and any homework I assigned to do with it.)

·In a half-page or
more: After re-reading the essay you wrote in class on Day 1.1 ("What is
evolution?") compose a letter to yourself highlighting what you were right
about and what you were wrong about or what was incomplete about your answer
based on what you learned this semester.

(Plus whatever we accomplished in class in the Skin Color workbook and any homework I assigned to do with it.)

·In a half-page or
more: Briefly describe what you learned this semester and what, if anything, it
means to you. Also, be sure to reflect on what you're still left wondering and describe
how you could find the answers to your remaining questions.

Extra credit!!! Make a time machine then go back to the
start of the semester, attend classes, take notes, read all of the things,
think about all of the things, complete the assignments, and study for the
quizzes.

You are a Homo sapiens. We are all Homo sapiens.

And no Homo sapiens who doesn’t know their species will be given a letter grade for this course.

Comments

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We've had to make a change to the commenting page. People had told us that Blogger was eating their comments, so now, rather than embedding comment editing with the posts, it has to be done on a separate, full page. Unfortunately, the 'reply' option has disappeared so comments will just follow one another. We'll see how this goes.